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  • 學位論文

基於微服務的SWMM雲端計算即時模擬系統之研發

Research of Microservice-based SWMM Cloud Computing Real-Time Simulation System

指導教授 : 林旭信
本文將於2026/02/22開放下載。若您希望在開放下載時收到通知,可將文章加入收藏

摘要


本研究以SWMM(Storm Water Management Model)模式作爲模擬核心,結合雲端計算(Cloud Computing)、微服務概念(Microservice)與IoT(Internet of Things)物聯網技術,以手機APP與Web APP作爲前端,開發SWMM雲端計算即時模擬系統(Microservice-based SWMM Cloud Computing Real-Time Simulation System,MBSS)。用於進行即時SWMM模擬,即時檢測雨水下水道水位,依為都市暴雨防災提供參考。 研究首先進行雲端水理微服務的開發,使用Python語言開發水理模擬核心,以Flask為框架進行微服務API的開發,再透過Docker容器對雲端水理微服務進行封裝。以亞馬遜AWS EC2作爲雲端計算服務平臺,將雲端水理微服務部署至EC2雲端。現地IoT物聯網裝置由壓力式水位計與Arduino、樹莓派組合成,將量測水位發送至雲端服務器。前端APP分別使用Django框架和Swift語言,開發Web APP與手機APP。 經水工實驗室水渠測試,由IoT裝置測量渠道水位,將水位數據發送至服務器。前端APP透過呼叫API的方式與雲端水理微服務進行交互,對SWMM水文參數進行設定,並發送模擬請求。雲端水理微服務根據使用者由APP提出請求,執行模擬,並將模擬結果與IoT設備測量數據進行對比,最後將結果返回至前端APP。通過不同流量的測試,雲端SWMM模擬實驗室水渠水位與水位計測量水位相近,顯示MBSS之可行性,可依為智慧型都市暴雨防災模擬預警系統之參考。

並列摘要


This research uses the SWMM (Storm Water Management Model) model as the core of the simulation. Combining Cloud Computing, Microservice and IoT (Internet of Things), also uses mobile APP and Web APP as the front-end to develop Microservice-based SWMM Cloud Computing Real-Time Simulation System(MBSS). MBSS is used for real-time SWMM simulation, real-time detection of rainwater channel water level, in order to provide reference for urban storm disaster prevention. This research begins with the development of cloud water management microservices, using Python language to develop the core of water management simulation, using Flask as the framework to develop microservice APIs, and then packing cloud water management microservices through Docker containers. Use Amazon AWS EC2 as the cloud computing service to deploy cloud water management microservices. The IoT device consists of a water level gauge, Arduino and Raspberry Pi, and sends the water level to the cloud server. The front-end APP uses Django framework and Swift language to develop Web APP and mobile APP. After the test in the hydraulic laboratory, the IoT device sends the water level data to the server. And the front-end APP interacts with the cloud water management microservices by calling the API, to set the SWMM hydrological parameters, and send simulation requests. The cloud water management microservice starts the simulation, compares the simulation result with the measured data form the IoT device, and returns the result to the front-end APP. Through the tests of different flow rate, the water level of the simulation is similar to the water level measured by the water level gauge, which shows the feasibility of MBSS. It can be regarded as a smart city rainstorm prevention simulation warning System reference.

並列關鍵字

SWMM Cloud Computing Microservices IoT Docker Container

參考文獻


參考文獻
Allende-Prieto, C, Mendez-Fernandez, BI, Sanudo-Fontaneda, LA and Charlesworth, SM (2018), “Development of a geospatial data-based methodology for stormwater management in urban areas using freely-available software,” International journal of environmental research and public health, 15, 8.
C Huber, W, E Dickinson, R and Barnwell, T (1988), “Storm water management model; version 4,” Environmental Protection Agency, United States.
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Edmondson, V, Cerny, M, Lim, M, Gledson, B, Lockley, S and Woodward, J (2018), “A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management,” Automation in construction, 91, 193–205.

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